Wireless network optimization

ABSTRACT

Methods and apparatuses are provided for optimizing a wireless network. A description of a traffic incident is received. An impact area is generated from the description. A geographic polygon is generated based on the impact area. The network usage of the geographic polygon is determined. A message including the network usage for the geographic polygon may be transmitted to a mobile network operator.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a divisional under 37 C.F.R. § 1.53(b) of U.S.patent application Ser. No. 15/251,302 filed Aug. 30, 2016 (AttorneyDocket No. 10171-16026A-US) now U.S. Pat. No. ______ which is herebyincorporated by reference in its entirety.

FIELD

The following disclosure relates to transportation systems and transitrelated applications, and more specifically to receiving events,determining an impact area, and calculating network loads.

BACKGROUND

As usage of connected devices increase, wireless networks that wereintended to support only a few devices, now support many more connecteddevices. In addition, the connected devices may be running bandwidthintensive applications such as streaming video. When designing awireless network, a mobile network operator (MNO) may use a peakbandwidth of all the devices to allocate network equipment for eacharea. If the peak bandwidth is defined by normal operation, incidentsthat cause an unexpected congestion of connected devices may result inthe wireless network dropping or ignoring requests. If the peakbandwidth is based on special or isolated incidents, for normaloperation, the capacity above the average usage is wasted bandwidth thatmay go unused. If a MNO can identify the incidents ahead of time andtake preemptive action, the peak bandwidth may be defined closer tonormal operation saving vast amounts of network equipment that wouldotherwise be deployed.

SUMMARY

In an embodiment, a method is provided for optimizing a wirelessnetwork. A description of a traffic incident is received. A geographicpolygon is generated based on the description. An estimated networkusage is calculated for the geographic polygon. The estimated networkusage is transmitted to a mobile network operator.

In an embodiment, an apparatus is provided for optimizing a wirelessnetwork. The apparatus comprises at least one processor and at least onememory. The apparatus is configured to receive a description of anincident. The apparatus generates a first impact area based on thedescription and a first geographic polygon based on the first impactarea. The apparatus generates a first bandwidth level for the a firstgeographic polygon. The apparatus generates a second impact area for asecond time period based on the description. The apparatus generates asecond geographic polygon and a second bandwidth level. The first andsecond bandwidth levels are transmitted to a mobile network operator.

In an embodiment, a method if provided for optimizing a wirelessnetwork. A description of an incident is received. An impact area isdetermined. A network data congestion level is determined for the impactarea. One or more cells that are associated with the network data levelcongestion level are identified. A message is transmitted to one or moredevices predicted to traverse the one or more cells.

BRIEF DESCRIPTION OF THE DRAWINGS

Exemplary embodiments of the present invention are described herein withreference to the following drawings.

FIGS. 1A and 1B illustrate an example of a wireless network covering aroadway network.

FIG. 2 illustrates an example system for optimizing a wireless network.

FIG. 3 illustrates an example map of a geographic region.

FIG. 4 illustrates an example geographic database of FIG. 2.

FIG. 5 illustrates example components of the geographic database of FIG.4.

FIG. 6 illustrates an example workflow for optimizing a wirelessnetwork.

FIG. 7 illustrates a map of an example roadway event.

FIG. 8 illustrates an example geographic polygon for the event of FIG.7.

FIGS. 9A, 9B, and 9C illustrate an evolution of the geographic polygonof FIG. 8.

FIG. 10 illustrates an example of a geographic polygon overlaid on acellular network map.

FIG. 11 illustrates an example location cloud of FIG. 2.

FIG. 12 illustrates an example workflow for optimizing a wirelessnetwork.

FIG. 13 illustrates an example cellular map and an example event.

FIGS. 14A, 14B and 14C illustrate example network usage maps.

FIG. 15 illustrates an example device of FIG. 2.

DETAILED DESCRIPTION

Embodiments are provided that assist in designing and managing anefficient wireless network by identifying and predicting network usagebased on traffic events. Wireless networks may be managed based oninformation relating to connected devices flowing through the wirelessnetwork. Network utilization requirements may be based on theinformation including the number and the usage profiles of the connecteddevices. The information may be used to create an infrastructure andsupport the connected devices through peak network demands. Byestimating the number of devices and their usage that result fromtraffic events, the wireless network may be managed to efficientlybalance the network loads using preemptive methods.

FIGS. 1A and 1B illustrates an example of a wireless network covering aroadway network. FIGS. 1A and 1B include three connected devices 11, 12,and 13 represented by the vehicles. The connected devices 11, 12, 13transmit and request information though a wireless cellular network. Thewireless cellular network includes a plurality of cells. FIGS. 1A and 1Balso include three cells 15, 16, and 17. Each of the cells 15, 16, 17may be serviced by one or more towers, base stations, and or networkequipment. While there may be overlap between the cells, each cell mayhandle the data load for every device inside the cell. As such, eachcell provides bandwidth for a maximum expected number of users.

The roadway 10 passes through each of the cells 15, 16, and 17. As theconnected devices 11, 12, and 13 pass through the cells 15, 16, and 17the connected devices 14 communicate with the respective base stationsin each cell 15, 16, 17. For FIG. 1A, the connected devices are spacedabout equally along the roadway 10. Connected device 11 communicatesusing the first cell 17. Connected device 12 communicates using cell 15;connected device 13 communicates using cell 16. If the vehicles werespaced equally apart, the base stations may only have to communicatewith one device at any point in time. The wireless network could bedesigned to only support a maximum of one device at a time. The examplehere using one connected device, however, in the real world, the basestations may support hundreds or thousands of connected devices at anytime.

In FIG. 1B, the connected devices 11, 12, and 13 have bunched up in cell16. Each of the connected device 11, 12, and 13 uses cell 16 tocommunicate. The congestion in cell 16 may be caused by, for example, anaccident or construction along the roadway 10. As a result of thecongestion, it is apparent that cell 16 may be required to providewireless networking to at least three connected devices, increase themaximum required bandwidth from the example in FIG. 1A by three times.In addition, due to the congestion, each connected device may betransmitting and requesting more information than normal. An autonomousvehicle, for example, may require additional map data to recognize anyalternation in the roadway 10. Further, as the incident that caused thecongestion in cell 16 may occur at any point along the roadway 10, eachcell 15, 16, and 17 may be required to support the maximum bandwidth ofthe three devices at all times, creating a surplus of bandwidth duringnormal operation. As illustrated in FIG. 1B, the cell 15 and 17 areunderutilized.

A solution to the bandwidth issues is to design the wireless network topreemptively transmit data to connected devices that are going to entera crowded cell in order to lighten the load on that cell. In theexamples of FIGS. 1A and 1B, each cell may be designed to support twoconnected devices. The example in FIG. 1B would overload cell 16 in sucha circumstance. However, if the wireless network was able to recognizethat congestion was occurring or going to occur in cell 16, the wirelessnetwork could preemptively use cells 15 and 17 to download or transmitdata to connected devices 11, 12, and 14 that are expected to enter cell16. Accordingly, once the connected devices 11, 12, and 14 do enter cell16, the connected devices 11, 12, and 14 may generate fewer requests andtake up less bandwidth allowing cell 16 to be managed with equipmentthat is only capable of supporting bandwidth for two devices. The savingfor each cell may be minor, but multiplied across the entire network thebenefit is substantial. Optimizing an existing wireless network as suchmay also delay the need to upgrade or replace equipment.

The following embodiments relate to a reception of events (e.g. fromvehicles or roadside sensors), filtering, processing, and transmissionof messages to mobile network operators (MNOs). The embodiments takeadvantage of accurate maps, real-time and historical traffic flowinformation and analytics to determine the impact that an incident willhave. The resulting analysis generates a geographic polygon based on thearea and roads that will be impacted by the incident. The predictednetwork usage of the geographic polygon may be determined based onhistorical network usage information. Preemptive actions may be taken tobalance the usage of the network based on the predicted network usageand event data. The polygon and predicted network usage, hascontinuously updated parameters, defined by ongoing impact analytics,based on the nature of the incident and the impact it will have overtime, that predict future network usage and requirements.

The disclosed embodiments may be implemented to optimize managing awireless network leading to an improvement in the computational system.The embodiments improve the efficiency and function of the cellularnetwork of a MNO. The increased efficiency and usage of resources maylead to less setup (fewer pieces of equipment), fewer communicationerrors, and less maintenance of the wireless network.

FIG. 2 illustrates an example system for optimizing a wireless network.The system includes one or more devices 122, a network 127, a cellularsystem 129, and a location cloud 121. The device(s) 122 are coupled withor wirelessly connected to the network 127 that are coupled with orwirelessly connected to both the cellular system 129 and the locationcloud 121. The location cloud 121 is coupled with the cellular system129. Herein, the phrase “coupled with” is defined to mean directlyconnected to or indirectly connected through one or more intermediatecomponents. Such intermediate components may include both hardware andsoftware based components. Additional, different, or fewer componentsmay be included. For example, in certain embodiments, system may includeadditional or different components such as a traffic management center.The cellular system 129 may include the network 127 or may manage a partof the network 127.

The device(s) 122 (also referred to as a connected device 122) may be amobile device or a sensor that provides samples of data for the locationof the device 122 or a vehicle. The device(s) 122 may be mobile phonesrunning specialized applications that collect location data as peopletravel roads as part of their daily lives. The device(s) 122 may also beintegrated in or with a vehicle. Applications, computer sub-systems orsensors that are either standard or optional equipment on a vehicle maycollect and aggregate information regarding the operation of thevehicle. The device(s) 122 may also be a sensor or a collection ofsensors, such as an inductance loop or optical detector (e.g., camera,light detection and ranging (LiDAR), or radar device). The device(s) 122may be a camera/imaging sensor for gathering image data (e.g., thecamera sensors may automatically capture traffic flow information,abnormal incidents, and/or traffic light information. The device(s) maybe sensors located on the perimeter of the vehicle in order to detectthe relative distance of the vehicle from lane or roadways, the presenceof other vehicles, pedestrians, traffic lights, potholes and any otherobjects, or a combination thereof. In one scenario, the sensors maydetect weather data, traffic information, or a combination thereof.

The device(s) 122 and/or other sensor(s) may be the means for collectingdata from one or more vehicles and aggregating the data into event dataincluding vehicle data or road condition data. The data may betransmitted over the network or through the cellular system 129. Thedevice(s) 122 may also be configured to receive data from the cellularsystem 129 through the network and or location clouds connected to it.The received data may include routing or navigation data, video data,audio data, or data relating to the device or vehicle.

A cellular system 129 may include one or more cellular network managersand one or more cellular boundary generators. The devices 122 maycommunicate with the cellular system 129 using the network 127. Thecellular system 129 may be operated by a MNO. An MNO may be a wirelessservice provider. The MNO may control the equipment necessary for awireless or cellular system to operate. The MNO may provide wirelesscommunications to a plurality of subscribers. The cellular system 129may include one or more cells including one or more pieces of networkequipment. In certain embodiments, the network equipment and cellularstructure may be part of the network 127 and controlled by the cellularsystem 129.

The cellular system 129 may be the means for identifying and storingnetwork usage for the entire network and or one or more of the cells.The cellular system 129 may store usage profiles for subscriber orconnected device 122. The cellular system 129 may identify and storenetwork usage patterns. The cellular system 129 may be configured toreceive information relating to a traffic event and or changes inweather or conditions affecting mobility, from the location cloud 121,and generate preemptive actions to balance or shift network traffic fromone cell to another.

The network 127 may include wired networks, wireless networks, orcombinations thereof. The wireless network may be a cellular telephonenetwork, LTE (Long-Term Evolution), 4G LTE, a wireless local areanetwork, such as an 802.11, 802.16, 802.20, WiMax (WorldwideInteroperability for Microwave Access) network, or wireless short rangenetwork such as Digital Short Range Communications (DSRC/802.11P).Further, the network 127 may be a public network, such as the Internet,a private network, such as an intranet, or combinations thereof, and mayutilize a variety of networking protocols now available or laterdeveloped including, but not limited to TCP/IP based networkingprotocols. The network 127 may be the means for transmission of databetween the devices 122, the cellular system 129, the location cloud121, and/or other equipment or devices in the system.

The location cloud 121 may include one or more servers, workstations,databases, and other machines connected together and maintained by amanager of connected vehicle data, including but not limited to map,sensor, wireless network, temporal climate and incident data. Thelocation cloud 121 (or traffic management system) may include one ormore server(s) 125 such as a sensor ingestion server, a trafficmanagement server, and/or an analytics server. The location cloud 121may also include a database 123. The location cloud 121 may beconfigured to provide up to date information and maps to external mapdatabases or mapping applications. The location cloud 121 collects oringests data from multiple sources, such as through the network 127, inorder to maintain up to date roadway conditions. Data such as sensordata, weather, road conditions, traffic flow, and historical data isprocessed to determine current and future traffic conditions.

The location cloud 121 may be the means for determining an incidenttype, an incident location, an impact area, and an incident durationfrom the collected data. The location cloud 121 updates the incident asadditional data is collected and the incident evolves over time. Theterm location cloud 121 is used herein to collectively include theingestion, analytic/computational, interface API's and messagedistribution capabilities residing in both local and cloud based systemsincluding the systems used for creating, maintaining, accessing, andupdating one or more database(s) 123.

The location cloud 121 may receive information relating to the networkusage from the cellular system 129, the network 127, or the devices 122.The network usage information may be real-time data or historical data.The location cloud 121 may be configured to estimate or predict currentor future network usage based on an impact of a traffic event and thenetwork usage information. The location cloud 121 may store theestimated or predicted network usage in the database 123. The locationcloud 121 may generate messages for transmission to devices 122describing preemptive actions to be taken to avoid over congested cells.

The database 123 (also referred to as a dynamic content database, sensoringestion database, traffic database, map database or geographicdatabase) may include geographic data used for network, traffic and/ornavigation-related applications.

In order to provide navigation-related features and functions to the enduser, the mapping system 121 uses the geographic database 123. Thegeographic database 123 includes information about one or moregeographic regions. FIG. 3 illustrates a map of a geographic region 202.The geographic region 202 may correspond to a metropolitan or ruralarea, a state, a country, or combinations thereof, or any other area.Located in the geographic region 202 are physical geographic features,such as roads, points of interest (including businesses, municipalfacilities, etc.), lakes, rivers, railroads, municipalities, etc.

FIG. 3 further depicts an enlarged map 204 of a portion 206 of thegeographic region 202. The enlarged map 204 illustrates part of a roadnetwork 208 in the geographic region 202. The road network 208 includes,among other things, roads and intersections located in the geographicregion 202. As shown in the portion 206, each road in the geographicregion 202 is composed of one or more road segments 210. A road segment210 represents a portion of the road. Each road segment 210 is shown tohave associated with the road segment 210, two nodes 212; one noderepresents the point at one end of the road segment and the other noderepresents the point at the other end of the road segment. The node 212at either end of a road segment 210 may correspond to a location atwhich the road meets another road, i.e., an intersection, or where theroad dead-ends.

FIG. 4 depicts an example geographic database 123. The geographicdatabase 123 contains data 302 that represents some of the physicalgeographic features in the geographic region 202 depicted in FIG. 3. Thedata 302 contained in the geographic database 123 may include data thatrepresent the road network 208. In the embodiment of FIG. 4, thegeographic database 123 that represents the geographic region 202 maycontain at least one road segment database record 304 (also referred toas “entity” or “entry”) for each road segment 210 in the geographicregion 202. The geographic database 123 that represents the geographicregion 202 may also include a node database record 306 (or “entity” or“entry”) for each node 212 in the geographic region 202. The terms“nodes” and “segments” represent only one terminology for describing thephysical geographic features, and other terminology for describing thefeatures is intended to be encompassed within the scope of the concepts.

The geographic database 123 may also include other kinds of data 312.The other kinds of data 312 may represent other kinds of geographicfeatures or anything else. The other kinds of data may include point ofinterest data. For example, the point of interest data may include pointof interest records including a type (e.g., the type of point ofinterest, such as restaurant, hotel, city hall, police station,historical marker, ATM, golf course, etc.), location of the point ofinterest, a phone number, hours of operation, etc. The geographicdatabase 123 also includes indexes 314. The indexes 314 may includevarious types of indexes that relate the different types of data to eachother or that relate to other aspects of the data contained in thegeographic database 123. For example, the indexes 314 may relate thenodes in the node data records 306 with the end points of a road segmentin the road segment data records 304. As another example, the indexes314 may relate point of interest data in the other data records 312 witha road segment in the segment data records 304.

FIG. 5 depicts some of the components of a road segment data record 304contained in the geographic database 123 according to one embodiment.The road segment data record 304 may include a segment ID 304(1) bywhich the data record can be identified in the geographic database 123.Each road segment data record 304 may have associated with itinformation (such as “attributes”, “fields”, etc.) that describesfeatures of the represented road segment. The road segment data record304 may include data 304(2) that indicate the restrictions, if any, onthe direction of vehicular travel permitted on the represented roadsegment. The road segment data record 304 may include data 304(3) thatindicate a speed limit or speed category (i.e., the maximum permittedvehicular speed of travel) on the represented road segment. The roadsegment data record 304 may also include data 304(4) indicating whetherthe represented road segment is part of a controlled access road (suchas an expressway), a ramp to a controlled access road, a bridge, atunnel, a toll road, a ferry, and so on.

Data for network usage, bandwidth restrictions, or connected devices 122may be stored as separate records 308, 310 or in road segment datarecords 304. The network usage data 308 and connected device data 310may include data such as network usage or profile data for one or moreconnected devices 122. The records 308 may include data relating to oneor more cells of a cellular network. The data for the one or more cellsmay include capacity for each cell as it relates to other information inthe geographic database such as road segment data records 304. Thegeographic database 123 may include road segment data records 304 (ordata entities) that describe features such as number of devices 304(5)or bandwidth estimations 304(6). The estimated bandwidth or peakbandwidth may be stored as a field or record using a scale of valuessuch as from 1 to 100 (1 representing low bandwidth, 100 representinghigh bandwidth), a value relating to throughput, or based on ameasurement scale such as data per second (Mb/s, Gb/s) or range thereof.The estimated bandwidth may be stored using categories such as low,medium, high. The estimated network usage or bandwidth may be receivedfrom the cellular system 129. The estimated network usage or bandwidthmay be derived from the number of expected vehicles that may be derivedfrom traffic flow and/or number of sensor vehicle identified on a roadsegment. The estimated network usage or bandwidth may be stored fordifferent time periods or events. For example, an estimated networkusage for a road segment may be stored for 15 minute increments over aday (for each day of the week). The estimated network usage may bestored for particular events such as construction or accidents.Additional schema may be used to describe the estimated network usage orbandwidth. The geographic database 123 may store other data 310 relatingto network usage such as data relating to non-vehicular connecteddevices 122. The connected device data 310 may store user profiles orestimated data usage for individual or groups of connected devices 122.The connected device data 310 may store usage patterns for connecteddevices 122 for different locations or time periods. Additionalgeographic and network usage data may be stores in other data 312. Theattribute data may be stored in relation to a link/segment 304, a node306, a strand of links, an area, or a region.

The geographic database 123 may store information or settings fordisplay preferences. The geographic database 123 may be coupled to adisplay. The display may be configured to display the roadway networkand data entities using different colors or schemes. The geographicdatabase 123 may store information relating to where bandwidth issuesconditions may exist, for example, though analysis of the data records,current/historical traffic conditions, cellular information, andcellular maps. Road segments with limited bandwidth or approaching abandwidth peak may be used to identify or supplement other data entitiessuch as potential hazards. Network usage or bandwidth data records alongwith geographic data records may indicate through a combination ofconditions that location on a roadway is not safe.

The road segment data record 304 also includes data 304(7) providing thegeographic coordinates (e.g., the latitude and longitude) of the endpoints of the represented road segment. In one embodiment, the data304(7) are references to the node data records 306 that represent thenodes corresponding to the end points of the represented road segment.

The road segment data record 304 may also include or be associated withother data 304(7) that refer to various other attributes of therepresented road segment. The various attributes associated with a roadsegment may be included in a single road segment record, or may beincluded in more than one type of record that cross-references to eachother. For example, the road segment data record 304 may include dataidentifying what turn restrictions exist at each of the nodes thatcorrespond to intersections at the ends of the road portion representedby the road segment, the name or names by which the represented roadsegment is known, the street address ranges along the represented roadsegment, and so on.

FIG. 5 also shows some of the components of a node data record 306 thatmay be contained in the geographic database 123. Each of the node datarecords 306 may have associated information (such as “attributes”,“fields”, etc.) that allows identification of the road segment(s) thatconnect to it and/or a geographic position (e.g., its latitude andlongitude coordinates or location in a cellular map or network). For theembodiment shown in FIG. 5, the node data records 306(1) and 306(2)include the latitude and longitude coordinates 306(1)(1) and 306(2)(1)for their node. The node data records include the cellular data306(1)(2) and 306(2)(2). The cellular data for each node may storesignal strength, number of connected device at a time period. Thecellular data may include the number and types of wireless networkequipment at the node or within a certain distance. The cellular datamay include thresholds for capacity or bandwidth. The node data records306(1) and 306(2) may also include other data 306(1)(3) and 306(2)(3)that refer to various other attributes of the nodes. The node datarecords, for example, may store network data usage, such as bandwidth ornumber of connected devices.

The geographic database 123 may be maintained by a content provider(e.g., a map developer). By way of example, the map developer maycollect geographic data to generate and enhance the geographic database123. The map developer may obtain data from sources, such as businesses,municipalities or respective geographic authorities. In addition, themap developer may employ field personnel to travel throughout thegeographic region to observe features and/or record information aboutthe roadway. Remote sensing, such as aerial or satellite photography,may be used. The geographic database 123 may receive data from acellular system 129 relating to the network usage or bandwidth for aroad segment, a cell, or an area. The geographic database 123 may storeone or more cellular maps that correspond to a wireless coverage area ofone or more cellular networks operating by one or more MNOs. Thecellular maps may include the configuration of the equipment in eachcell of the cellular maps including information such as the transmissioncapacity of each cell.

The geographic database 123 may store data relating to an impact area ofa traffic event. The geographic database 123 may store generatedpolygons for different types of traffic events and information relatingto how the generated polygons change over time. The geographic database123 may store information relating to how the traffic event affectsnetwork usage inside the generated polygon.

The geographic database 123 and the data stored within the geographicdatabase 123 may be licensed or delivered on-demand. Other navigationalservices or MNOs may access the traffic data and the network usage datastored in the geographic database 123. Data including the network usagedata for a link or cell may be broadcast as a service.

FIG. 6 illustrates a flow chart of a method for wireless networkoptimization. As presented in the following sections, the acts may beperformed using any combination of the components indicated in FIG. 2.The following acts may be performed by the cellular system 129, thelocation cloud 121, or a combination thereof. Additional, different, orfewer acts may be provided. The acts are performed in the order shown orother orders. The acts may also be repeated.

At act A110, the location cloud 121 receives a description of a roadwayevent or incident through the network 127. The roadway event may includea roadway description, description of the incident and or roadcondition, a roadway location, and a time. Roadway events may bedetermined from a variety of sources. The roadway event may originatewith an end user manual input, an automated response from the collectiondevice(s) 122 such as a probe or sensor. A device 122 may be configuredto collect data from multiple sources to create a roadway event. Avehicle (or device(s) 122 integrated into vehicles) may transmit dataregarding their operation or roadway conditions over the network. Remotesensing, such as aerial or satellite photography may be used to collectinformation. Field personal may report roadway conditions that may becollected automatically or manually. Roadway events may be collectedfrom social networks or from other internet based sources. A device 122may also be used as one or more probes or sensor(s) to collect roadwaydata. Roadway events may include a roadway location (from GPS,navigational device, mobile devices, or other devices that may collectpositional data) and a time of the event (or a time of transmission).

A roadway event may be derived or predicted from received information.Increased traffic congestion may be predicted based on an increase in anumber of routes that specify a roadway segment or node. For example,the location cloud 121 may provide routing services to one or moredevices. The location cloud 121 may predict based on the requested orreceived routes that a road segment may see abnormal traffic. Connecteddevices 122 may for example transmit routes to the location cloud 121.The collected routes may be analyzed to determine any future abnormaltraffic pattern. Any abnormal traffic pattern may lead to (or be) aroadway event. Other abnormal traffic patterns, for example for aspecial event (concert, sporting event, etc.), may be predicted by thelocation cloud 121 without receiving a description of a roadway eventfrom a device. Descriptions of a roadway event received from a device orsensor may be used to supplement or fine tune descriptions of eventgenerated from routing or special event data.

FIG. 7 illustrates an example roadway event at location 55. FIG. 7includes a roadway network including nodes A through J. FIG. 7 alsoincludes road segments that are identified by the nodes at either end,for example road segment AD is the road segment illustrated betweennodes A and D; road segment DH is the road segment between nodes D andH; and so on. The roadway event occurs at location 55. The roadway event55 may be for example, a construction event or an accident. The roadwayevent 55 may be detected or identified and or communicated by a device122 or sensor. The description of the roadway event may be generated bycollected data from multiple devices or sensors. For example, vehicleson road segments HI, DH, and GH may all report slower than typicalspeeds. The roadway event 55 may be derived from the combination of thereports. A description of a roadway events may include a wide range ofroad information, from accidents, weather conditions, obstructions tocongestion and delays. Roadway descriptions may also deal with publictransport from rail, bus to air traffic and ferry services. Otherroadway events or roadway descriptions might include: vehicularoperation (such as brake lights, slowing of speed, windshield wiperoperation, or other vehicular sensors monitoring the vehicle'soperation), construction, accidents, roadway closures, roadwaycongestion, roadway speeds, roadway surface condition, roadwayenvironmental condition, roadway traversal energy information (for usein green routing applications), or other ‘non-normal’ roadwayconditions. The descriptions may reference current conditions or priorconditions.

At act A120, the location cloud 121 generates a geographic polygon basedon the description. The geographic polygon may be based on an impactarea that is derived from current and historical flow data and probebased analytics stored in the database. The impact area may include anyareas that are predicted to have altered traffic patterns or congestiondue to the traffic incident. The location cloud 121 may identify animpact area for the incident. From the impact area, the location cloud121 may generate the geographic polygon (also referred to as a polygon).

The roadway description may be an update to a previously received event,such as an updated event with current description of vehicular operation(potentially including a current speed). Each roadway event isclassified as either relating to a previous incident or a new incident.Multiple roadway events may be classified as relating to a singleincident. For example, the location cloud 121 may receive multipleevents regarding an accident, from visual (images) to audio to reportsindicating that multiple vehicles have slowed. The events may becollected from different sources over a period of time. The locationcloud 121 processes each event to determine if the event relates to acurrent ongoing incident. Each additional event that is related to theongoing incident is part of the incident information flow and may affectan impact area or geographic polygon as the incident evolves over time.

In addition to receiving events from different sources, the locationcloud 121 may receive multiple events from a single source. In such anexample, a new event may update a previously received event. Forexample, a first event may indicate that a vehicle is slowing orstopped. A second event received subsequently from the same vehicle mayindicate that the vehicle is moving at an expected speed. Depending onthe information included, a subsequent event may indicate that anincident has passed and that network usage has returned to normaloperation. In other embodiments, subsequent events are used by thelocation cloud 121 to evolve or update an impact area as explainedbelow.

The location cloud 121 may use data from multiple events as mentionedabove. The location cloud 121 may also analyze data regarding weatherconditions, road conditions, current traffic conditions, and historicaltraffic conditions. The historical traffic conditions may also includeprior event data. The impact area is generated by looking at current andhistorical traffic conditions and comparing how events affect thesurrounding roadway network. For example, a previous event (such as anaccident) may have affected the road segment where the accidentoccurred, but also may have affected multiple other surrounding areas.The impacted area may also be affected by the time of day, the weatherconditions, among other factors.

The impact area may only be identified for areas when an alteration ofthe traffic pattern exceeds a threshold level. For example, there may bethousands to millions of traffic incidents that cause minor trafficpattern alterations. A single vehicle that slows at a location may causea slight increase in congestion for a time period. If the congestiondoes not increase or decrease above or below a threshold level, theincident may be temporarily ignored. Minor incidents may be combined todetermine a larger incident that exceeds the threshold levels. Athreshold level for an increase in congestion may be determined by apercentage or by a predetermined level of congestion. For example, aroad segment may have levels of congestion designated at each 100vehicles (100, 200, 300, 400 . . . ). A threshold increase or decreasemay be set at the increase or decrease crossing multiple levels. Forexample, an increase from the 100 to 200 level may not be identified asan impact, however, an increase from the 200 level to the 400 level mayrepresent an impacted road segment. Alternative schema for determining athreshold may be used.

The location cloud 121 may use analytics to generate a geographicpolygon (“polygon”) based on the roads that will be impacted by theincident. The polygon may be an outline of the impact area. The polygonmay be a collection of road segments that are impacted by the incident(e.g. at least partially in the impact area). The polygon may bedescribed using geographic coordinates such as latitude and longitude.The polygon may be a closed shape including three or more straightlines. The polygon may be a circle or oval.

FIG. 8 illustrates a geographic polygon 61 for the incident 55 of FIG.7. For the example incident in FIG. 7, the location cloud 121 maydetermine that segments DH, GH, HI, IF, and IJ may be affected for acertain time period. The generated polygon 61 in FIG. 8 illustrates thearea that may see increased or decreased traffic flow or congestion as aresult of the incident. The polygon 61 may be a simple representation ofan area that has altered traffic flow or congestion. Multiple polygonsmay be used to specify the type of alteration. A first polygon, forexample, may represent an area with a first level of increasedcongestion. A second polygon may represent an area with a second levelof increased congestion. A third polygon may represent an area with afirst level of decreased congestion. The levels of increased ordecreased traffic effects may be stored at the road segment level. Forexample, for the polygon 61 in FIG. 8, the data may be stored as DH (1),GH (4), HI (6), IF (8), and IJ (4) using a notation of [segment (trafficcongestion increase)]. Alternative schemes for storing the polygon andrelated data may be used.

In certain embodiments, more than one polygon may be generated for theincident. The location cloud 121 may, for example generated multiplepolygons for an incident that describe the impact over time. Thelocation cloud 121 may generate multiple polygons that describedifferent levels of impact as a result of the incident. For example, thepolygon illustrated in FIG. 8 may represent the area that has anydecreased or increased congestion. One or more different polygons may begenerated that represent the areas with different levels of increases.For example, a smaller polygon may be generated that represents an areathat is more affected by the incident than outlying areas (or, forexample, the major roadways that include more traffic to start with andmay have a greater increase in congestion).

As additional events are received by the location cloud 121, the polygonis shaped in real time based on incident information flow. Eachadditional event within the information flow that is related to anincident is filtered and processed. The information from each additionalevent may be used to adjust an impact area. For example, a trafficcongestion incident may worsen over time. The initial event may indicatethat a certain area will be affected. However, as additional events fromother vehicles are received and analyzed, the impacted area may evolveover time. A larger or smaller area may be affected. In certainembodiments, even without additional events, the impact area may bealtered over time. Using historical data, the location cloud 121 maypredict the evolution of an incident without additional real-time inputsof data. For example, a traffic congestion incident at a knownintersection at a certain time under certain road and weather conditionsmay dissipate at a known rate based on historical data. The locationcloud 121 may predict that the incident may affect an outlying roadsegment, but only for a limited amount of time. The polygon will includethat road segment initially, but the polygon may evolve over time andsubsequent generated polygons may not cover the road segment or cell.

FIGS. 9A, 9B, and 9C illustrate an evolution of the polygon 61 of FIG. 8over time (T=1, 2, 3). FIG. 9A illustrates a first polygon 61 generatedas a result of the incident at location 55. The polygon 61 includessegments DH, GH, HI, IF, and IJ as defined by the nodes of FIGS. 7 and8.

At a subsequent time, a second polygon 62 is generated with an updatedimpact of the incident. The second polygon is depicted in FIG. 9B.Segments IF and IJ have returned to a normal traffic flow while segmentAD has been added to the polygon 62. The second polygon 62 may begenerated as a result of receiving a second description of the trafficincident. The second polygon 62 may also be generated as a result ofmachine learning or predictions made from similar historical trafficincidents. If a similar incident has occurred at the location 55 at asimilar time, the second polygon 62 may be generated from actualprevious data. If there has not been a similar incident at the location55 at a similar time, the location cloud 121 may estimate the resultbased on other incidents at similar locations. Additional real timeinformation received from devices 122 in the area may also affect thesize of the polygon as the polygon is updated.

At a subsequent time, a third polygon 63 is generated with an updatedimpact of the incident. The third polygon 63 is depicted in FIG. 9C. Thesegment AD that was affected in FIG. 9B has now returned to normaltraffic flow. The segments DH, GH, and HI still remain affected at thistime. Additional polygons may be generated until the effect of theincident has subsided.

In order to generate the one or more polygons, the location cloud 121uses historical as well as real-time traffic and incident data. The dataprovides an element of predictive analytics around creation of apolygon. Historical traffic data (e.g. speed or flow or volume) may bestored within the database alongside each road segment or node. Eachroad segment may contain historical traffic data for each of weatherconditions, road conditions, or time of day. The traffic data for eachsegment may be updated as new data is processed. To decrease latency forcomputing the polygon, segments may be grouped into classificationsdepending on the segments their historical traffic patterns or commonattributes. For example, certain road segments that are similar mayshare a common profile instead of having separate historical profiles.Other data beyond traffic data for road segments or nodes may also beused as historical data. Historical traffic patterns and flow volume mayalso be stored for road segments or geographic locations. Historicaltraffic data may also be saved, archived or organized event by event (orincident by incident). For example, a roadwork warning in a certainlocation may be a repeatable event for not only that location but alsoother similar locations. The events and the subsequent impacts may bestored in the database to be used both as models for specific locationsand also as models for similar events occurring at different locations.

Driver tendencies may also be used to provide additional informationthat could influence the predicted analytics and subsequent impact areagenerated from said data. For example, different drivers in differentregions or countries make different decisions when confronted withcertain obstacles or conditions. The decisions may be stored and used topredict how an impact area may evolve over time.

The polygon may be shaped by the presence of alternative routes. Trafficflow may be modelled based on the historical data, but also may dependon alternatives routes or bypasses. If there is an alternative route,the impact area of an incident may be smaller, and may dissipate quickerthan if there are no alternative routes. Further, the location cloud 121may take into account how many vehicles are guided by navigation systems(that might affect the routing of those vehicles). For example, vehiclesthat received traffic alerts may be more likely to take an alternativeroute that in turn may alter the shape of the polygon over time.

The polygon may be created using the map database and the road segmentsand nodes contained within the database. The polygon may be createdusing a geographic coordinate system such as latitude and longitude. Forexample, the boundary of the polygon may be described using geographiccoordinates. The polygon may also be described using a set or list ofroad segments or nodes. The polygon may extend for a certain distancealong a road segment (possibly with an offset from a starting node). Thepolygon may be a combination of two or more areas that overlap. Thepolygon may include both directions of a roadway or just a singledirection. The polygon may exclude areas such as parking lots or otherareas that are not affected.

Once polygons are created, the polygons can be archived for future useto reduce latency around analytics necessary to generate said polygon. Apolygon may be saved once a correlation is made between the incident andlocation. The evolution of the polygon over time may also be archivedand used in future predictions.

In certain embodiments, the polygon may be stored with the associatedimpact on traffic. For example, for each road segment or polygon, theincreased or decreased level of congestion or traffic flow may beidentified and stored in the database.

The location cloud 121 may be configured to identify a plurality ofevents and determine the impact the events. For example, two distincttraffic events may overlap in their impact area. The combined impact ofboth events may affect the total impact. A first event may causecongestion on a roadway. A second event may alter the impact pattern ofthe first event. The combined impact may be different that either thefirst or second impact separately. A polygon may be generated for eachevent and stored. When both event occur during the same time period, athird polygon may be generated that combines the effects of the firstand second events.

At act A130, an estimated network usage (or load) is calculated for thegeographic polygon based on the description. The estimated network usagemay be calculated by the cellular system 129 or the location cloud 121.The location cloud 121 may, for example, transmit the impact area to thecellular system 129 that may then be compared with cellular coverage todetermine which cells are affected. The cellular system 129 may usehistorical and/or real time network usage data to determine the effectof the incident on network usage. In certain embodiments, the locationcloud 121 may receive and store historical network usage data from thecellular system 129. The location cloud 121 may estimate current andfuture network usage using the traffic data and the historical networkusage data. The estimated network usage may be a value relating to theestimated network usage or an indication of an increase or decrease innetwork usage (e.g. bandwidth or connected devices).

The estimated network usage may be proportional to the number and typeof connected devices 122 in the impact area. The number and type ofdevices 122 may be calculated using an identified number of vehicles inthe area (for example by extrapolating the total number of vehicles andconnected devices 122 based on the number of sensor devices or detecteddevices). The number of identified vehicles may correlate with a totalbandwidth. The location cloud 121 or the cellular system 129 maygenerate the estimated network usage using historical networkinformation that relates to a number of vehicles or connected devices122 in an area.

In certain embodiments, one or more profiles of the connected devices122 may be used to estimate the network usage. Certain device types orusers may generate more or less bandwidth. A bus for example may, as aresult of carrying multiple devices, may use much more bandwidth than asingle passenger vehicle. The location cloud 121 or cellular system 129may store usage profiles for one or more of the connected devices 122.The usage profiles may be used to estimate the network usage granularlyor by, for example, sorting the connected devices 122 into groups thatrelated to averaged out bandwidth usage. The bus in the example above,may be included in a group that exhibits very high bandwidth usage. Amobile phone may be included in a group that exhibits low bandwidth. Amobile phone for a user that streams high levels of video may beincluded in a group for medium bandwidth usage.

FIG. 10 illustrates an example of the geographic polygon overlaid on acellular network map. FIG. 10 includes the roadway network illustratedin FIGS. 7 and 8. FIG. 10 overlays the roadway network and thegeographic polygon to a cellular map that contains cells C81-C92. Eachof the cells may include at least one transmitter or base station. Incertain embodiments, the cellular map may include transmitter ranges foreach of the cells. A map may include the range of each transmitter overa geographic area. The map may include the range of one or more basetransceiver stations (BTS) or one or more connected Evolved UMTSTerrestrial Radio Access Network (E-UTRAN) Node B stations.

Each cell (base station) may be configured to transmit and receive datafrom the devices 122 within range of the base station. The cells C81-C92in FIG. 10 are shown as hexagonal shapes. There may be overlap betweenthe cells. As a device 122 travels from one cell to an adjacent cell thecommunications may be handed off between base stations. The size andrange of the cells may vary with locational requirements orobstructions. Each cell may have a maximum throughput, load, orbandwidth for transmitting and receiving data.

The estimated network usage may be assigned to individual cells of acellular network. The geographic polygon may be overlaid on a cellularmap to determine which cells will be impacted by the incident. Thechange in congestions or number of connected devices 122 for eachconnected cell may then be derived from the traffic data.

The cellular map may have been previously generated or received from acellular system 129. The cellular map may be generated by a cellboundary generator. The cellular map may be a geographic map thatindicates the coverage area for each individual cell. Different wirelessproviders may have different cellular maps. For example, a firstprovider may use different towers than a second provider and as suchhave different coverage maps. The location cloud 121 may overlay thepolygon on each individual cellular map. The cells that include a partof the polygon may be affected by the incident. There may be a thresholdfor how much overlap is required to specify a cell as impacted. Forexample, the polygon may have to cover at least a predeterminedthreshold amount of the cell. The predetermined threshold amount may be5%, 10%, 50% or another value between 0 and 1.

As shown in FIG. 10, the cells affected by the geographic polygoninclude C86, C87, C88, C89, and C90. The geographic polygon covers aportion of cell C92, but not enough to cover, for example, a 50%threshold level. In certain embodiments, a certain percentage of thecell may be required to be covered to be identified as affected. Thelevel of increase congestion may also be taken into consideration fordetermining if a cell is affected. In the example of FIG. 10, ifcongestion was predicted to increase drastically in the portion of cellC92, then cell C92 may be identified as impacted.

The estimated network usage may be calculated from the increased ordecreased traffic data. Additional vehicles or devices 122 in an areamay correspond to additional network usage. In certain areas, thenetwork usage may be primarily affected by the number of vehicles in thearea. For example, a cell that covers a section of highway may onlytransmit to a few devices other than either connected vehicles orconnected devices 122 that are in or related to the vehicles. In otherareas, connected vehicles or connected devices 122 that are in orrelated to the vehicles may make up a smaller percentage of the totaldevice but still may be responsible for any increase or decrease. Forexample, a cell in an urban area may transmit with office workers orpedestrians. The number of devices may not fluctuate that far fromnormal due to the ebb and flow of pedestrians. However, the number ofdevices may increase dramatically if the number of vehicles (that stillonly make up a small portion of the connected devices) increase.

The increased or decreased traffic data may be generated when thegeographic polygon is generated. The estimated network usage may becalculated directly using historical or current traffic. The estimatednetwork usage may be calculated using historical network usage data.Network usage may be tracked over time for each cell. The network usagemay then be stored and compared against events that occurred. Thehistorical network usage and associated events may then be used topredict or estimate current or future network usage for the area.

In certain embodiments, the estimated network usage may vary separatelyfrom the geographic polygon. The geographic polygon may be a singleinput among many in the calculated of the estimated network usage. Forexample, the estimated network usage may be used to generate a loadbalancing scheme that shifts downloads or transmission of data todifferent cells. As such, a new or updated estimated network usage maybe generated for both the original and newly affected areas (e.g.cells).

The cellular map and the effect on each of the cells may be stored inthe database for each type, location, and timing of an incident.

At act A140, the estimated network usage is transmitted to a MNO. A MNOmay be responsible for managing a wireless network. In certainembodiments, the estimated network usage may be compared to a baselinenetwork usage. The cells or areas that exceed the baseline network usagemay be reported to the mobile network operator. The amount that theaffected cells or areas are estimated to exceed the normal operationsmay also be transmitted. The mobile network operator may performpreemptive moves to guarantee data service levels necessary to meet peakdemands.

In certain embodiments, the MNO may request that connected devices 122that are expected to enter the affected cells to perform tasks that maylimit the connected device's 122 exposure to the affected cells. Theconnected devices 122 may, for example request and receive map dataahead of time. The connected devices 122 may buffer a video or audiotrack. The connected devices 122 may attempt to connect to analternative method for transmission such as a local Wi-Fi station or adifferent cellular provider.

In certain embodiments, an autonomous vehicle may take action when theaffected cells are identified. The autonomous vehicle may avoid the areaif the affected cell cannot guarantee transmission to the autonomousvehicle. As described herein, an autonomous driving vehicle may refer toa self-driving or driverless mode that no passengers are required to beon board to operate the vehicle. An autonomous driving vehicle may bereferred to as a robot vehicle or an autonomous driving vehicle. Theautonomous driving vehicle may include passengers, but no driver isnecessary. Autonomous driving vehicles may park themselves or move cargobetween locations without a human operator. Autonomous driving vehiclesmay include multiple modes and transition between the modes.

As described herein, a highly assisted driving (HAD) vehicle may referto a vehicle that does not completely replace the human operator.Instead, in a highly assisted driving mode, the vehicle may perform somedriving functions and the human operator may perform some drivingfunctions. Vehicles may also be driven in a manual mode that the humanoperator exercises a degree of control over the movement of the vehicle.The vehicles may also include a completely driverless mode. Other levelsof automation are possible.

The autonomous or highly automated driving vehicle may include sensorsfor identifying the surrounding and location of the car. The sensors mayinclude GPS, light detection and ranging (LIDAR), radar, and cameras forcomputer vision. Proximity sensors may aid in parking the vehicle. Theproximity sensors may detect the curb or adjacent vehicles. Theautonomous or highly automated driving vehicle may optically track andfollow lane markings or guide markings on the road.

Autonomous or highly automated driving vehicle may require highdefinition up to date maps. Bandwidth requirements for servicing anautonomous vehicle or HAD may be high relative to the normal usage of aconnected device 122. Without the high definition maps, the autonomousvehicle may not be able to operate efficiently. The high definition mapsmay be downloaded ahead of time, however, environments change, anddownloading a map too far ahead of time may lack certain features thathave changed. The closer to a feature or roadway segment that the map isdownloaded, the more likely the map is up to date. However, due to thebandwidth requirement, in the case of a traffic incident, if everyvehicle attempted to download a high definition map, the cell's peakbandwidth may be achieved. Using the estimated network usage, a cellularsystem 129 or a location cloud 121 may request that one or more vehiclesdownload the maps from a cell that is not currently affected by anincrease in congestion. The cellular system 129 or location cloud 121may spread out the downloads over multiple cells for multiple vehiclesto lessen the load on any individual cell of the wireless network.

FIG. 11 illustrates an example location cloud 121 of FIG. 1. Thelocation cloud 121 includes a server 125 and a database 123. The server125 may include a sensor incident ingestion module 813, real-time andhistorical traffic flow and incidents module 807, a weather module 809,a road conditions module 811, a network usage module 815, a processor800, a communications interface 805, and a memory 801. The processor 800is connected to the database. Each of the modules may be included withinthe processor 800. Each of the modules may also be connected to thedatabase in order to access current and historical geographical andnetwork usage data.

The server 125 and associated modules are configured to receive an eventmessage using the communications interface 805. The event message mayinclude a description of an incident. The server 125 and the modules areconfigured to generate an impact area of the incident using current andhistorical traffic data stored in memory 801 and the database 123. Theserver 125 is configured to generate a geographic polygon based on theimpact are of the incident. The server 125 is configured to identify theeffect the incident has on the network usage in the impacted area. Theserver 125 may be configured to transmit the geographic polygon or thenetwork usage to a MNO or one or more device(s) 122 located within thegeographically using the communication interface 805. The server 125 maybe configured to generate an updated impact area for a later timeperiod. The server 125 may be configured to generate an updatedgeographic polygon and identify the effect the incident has on thenetwork usage at the later time period. The server 125 may be configuredto communicate the network usage and preemptive measures to one or moreconnected devices 122.

The processor 800 may include one or more of the sensor incidentingestion module, the real-time and historical traffic flow andincidents module, weather module, and/or road conditions module. Theprocessor 800 may be the means to receive collected data from thedevice(s), determine an event, and generate an impact area and incidentevent. The processor 800 may be configured to request and receive datathrough the communication interface. The processor 800 may also beconfigured to access the database including current and historical data.The processor 800 may be configured to use the geographic maps togenerate a precise impact area.

The real-time and historical traffic data and incidents module 807 (ortraffic module) may be the means for modeling current and future trafficdata in an area based on historical data, incident events, and otherroad event data. The traffic module 807 may store current and futuretraffic data predictions for later use to decrease latency if futuresimilar incidents occur. The traffic module 807 may estimate or predictthe traffic impact of incidents or events.

The weather module 809 receives and processes weather data. The weathermodule 809 may collect weather data from sensors such as in-vehiclesensors, other on-ground sensor, weather services, or third partysources. The weather module 809 may generate messages regarding weatherconditions at specific locations such as fog (low visibility), rain,snow, wind, among others.

The road conditions module 811 receives and processes road conditiondata. The road conditions module 811 may collect road condition datafrom sensors such as in-vehicle sensors, field personal, third partysources, or other collection apparatus or services. The road conditionsmodule 811 may generate alters regarding road conditions such as icyroads, flooding, certain construction issues, among others.

The sensor incident ingestion module 813 may be configured to collect oringest data from one or more collection devices or other sources usingthe communications interface 805. The sensor incident ingestion modulemay be configured to determine if an event is part of an ongoingincident or if the event is a new incident.

The network usage module 815 may be configured to identify a networkload for one or more cells of a cellular network. The network load mayrelate to bandwidth or a number of connected devices. The network usagemodule 815 may collect and store network usage data received fromconnected devices 122 or a cellular system 129. The network usage module815 may be the means for estimating current or future network usagebased on traffic data and historical and current network usage for oneor more impacted cells of a cellular network.

FIG. 12 illustrates an example workflow for optimizing networktransmissions using the location cloud 121 of FIG. 11. As presented inthe following sections, the acts may be performed using any combinationof the components indicated in FIG. 2. The following acts may beperformed by the cellular system 129, the location cloud 121, or acombination thereof. Additional, different, or fewer acts may beprovided. The acts are performed in the order shown or other orders. Theacts may also be repeated.

At act A210 a description of an incident is received. The incident maybe a traffic incident such as an accident, a roadway condition, or anyother traffic event. An incident in the future may be predicted usinginformation relating to route requests, routes or expected positionsreceived from connected devices, special events, information derivedfrom social media, or other navigations applications. The descriptionmay be received from one or more devices 122 or sensors that monitor aroadway network. Multiple reports may be generated by the one or moredevices 122 or sensors that relate to a single incident. The descriptionof the incident may be generated from the multiple reports.

At act A220 an impact area of the incident is determined based on thefirst description. The impact area may be generating using a combinationof current and historical traffic data cross referenced with previouslyreceived descriptions of similar incidents. The impact area may begenerated using routing information for vehicles on a roadway network.The impact area may be defined by a geographical polygon that includesthe area or areas that have areas that are impacted by the traffic. Theimpact area may include one or more road segments, one or more nodes,and/or one or more partial road segment. The impact area may alsoinclude areas adjacent to road segments or nodes.

One or more impact areas may be generated for the incident. For example,multiple impact areas may be generated for different time period for asingle incident. Each impact area may correspond to an estimated impactat a certain point, e.g. the time of the incident (T), T+15 minutes,T+30 minutes, T+45 minutes, and so on. Alternative time periods may beused depending on the nature and type of the incident and the subsequentimpact.

At act A230, a network data congestion level is determined for theimpact area. The network data congestion level may relate to a currentlevel of network congestion or a future estimated level of networkcongestion. Historical network data may be collected and stored. Networkdata may include data relating to a number of connected devices 122, theusage of the connected devices 122, and/or the total capacity of one ormore base stations or cellular towers. The historical network data andthe traffic data may be used to determine a network data congestionlevel. The traffic data may predict an increase in congestion or trafficflow in the impact area. The congestion or traffic flow may indicate anincrease in connected devices 122 (more vehicles may indicate moreconnected vehicles or personal devices associated with or in thevehicles) or the use of the connected devices 122 (unexpected events maycause an increase in requests for information from the connected devices122). A profile or type for the connected devices 122 may be identifiedand used to determine an estimated bandwidth level. The estimatedbandwidth level (or data congestion level) may be compared to athreshold level. The threshold level may be determined by a magnitude ofchange in congestion or bandwidth predicted by the impact area. Forexample, an impact area may indicate an increase of congestion, but notto a level that would affect the wireless network. Alternatively, aminor increase in an already congested impact area may satisfy thethreshold.

At act A240, one or more cells of a cellular network that are associatedwith the network data congestion level change are identified. The one ormore impact areas may correspond to one or more cells of a cellularnetwork. A cellular network may include one or more cells that transmitand receive data to and from connected devices 122. Each cell mayinclude one or more base stations that have a range for transmission.The range of each cell may be mapped and overlaid on a roadway networkto identify the cells that correspond to the impact area.

FIG. 13 illustrates a cellular map 91 including multiple cells overlaidon a roadway network 93, the roadway designated by the dotted lines. Anevent occurs at location 55. An impact area and/or geographic polygonmay be generated from the description of the event at act A220. Theimpact area may represent an area at a current time or future time wherethe traffic flow or congestion has been altered from a normal state bythe event. The impact area may be derived from historical traffic datafor similar events. Traffic data for events may be collected as theevents occur and then stored in a database. Using the stored data, thetraffic flow and congestion for current and future events may bepredicted using machine learning. The network data congestion level maybe determined at act A230. Alternatively, one or more cells may beidentified that relate to the impact area and then the network datacongestion level is determined for the one or more cells.

FIGS. 14A, 14B, and 14C illustrate three network maps for multiple cellsas a result of a traffic incident. The sequence of how the estimatednetwork usage changes may be stored in the database. FIG. 14A depicts asmaller area including four cells that are impacted by the incident inFIG. 13. For one cell, colored black, the network data congestion ishighly impacted by the incident. As the event evolves over time, thegeographic polygon may change shape and as such, the overlay on thecellular map may change the affected cells. Each cell may be affecteddifferently. FIGS. 14B and 14C depicts three different levels of change.FIGS. 14B and 14C depict how the incident in FIG. 13 grows to includemore and more cells. The incident may impact the data congestion levelfor cells in a different way. For example, a major increase incongestion. Certain cells may only see a minor increase in congestion.Other cells may see a decrease in congestion. The network mapsillustrated in FIGS. 14A, 14B, and 14C track the way an incident impactsthe cells of a cellular network. The network maps may be stored with theincident description and any related traffic information that is used topredict the increase or decrease in congestion. The stored maps may beused later to assist in analyzing future events.

Each of the cells may include a threshold level of bandwidth. Adetermination that the threshold level is not met may indicate that theparticular cell is not impacted enough to generate a message below atact A250. The cell may then be determined to not be impacted even thoughthere is a change in congestion (either traffic or data). The thresholdmay be a percentage of the maximum bandwidth. For example, a cell thathas a maximum capacity of 100 Mb/s may be determined to be impacted onlyif the estimated usage rises above 80 Mb/s. Alternatively, even if theestimated usage rises from, for example, 20 Mb/s to 40 Mb/s (a 100%gain), the cell may not be determined to be impacted because there is norisk of exceeding the capacity and degrading of wireless service.

At act A250, a message is transmitted to one or more devices 122predicted to enter the one or more cells. The one or more devices 122may correspond to one or more vehicles on the roadway network. Theroutes of the one or more vehicles may be identified. In certainembodiments, the routes may have been generated and transmitted to thedevices 122 by a navigation system or the location cloud 121. Devices122 may receive instructions to preemptively download or access datathrough a cell that is not impacted or less impacted than other cells.

FIG. 15 depicts an example device 122 configured to optimize a wirelessnetwork. The device 122 may be the means for transmitting and receivingtraffic related data. The device 122 may be configured to generate orreceive a route and display the route for a user. The device 122includes an input device 403, a communications interface 405, acontroller 400, a memory 404, position circuitry 407, and an outputinterface 411. The device 122 may be configured to receive a networkcongestion message from a cellular system 129 or location cloud 121. Thedevice 122 may be configured to take preemptive actions relating towireless usage such as buffering or preemptively downloading maps,traffic or transportation related data.

The memory 404 and/or memory 801 may be a volatile memory or anon-volatile memory. The memory 404 and/or memory 801 may include one ormore of a read only memory (ROM), random access memory (RAM), a flashmemory, an electronic erasable program read only memory (EEPROM), orother type of memory. The memory 204 and/or memory 801 may be removablefrom the mobile device 122, such as a secure digital (SD) memory card.The memory may contain a locally stored map database.

The controller 400 and/or processor 800 may include a general processor,digital signal processor, an application specific integrated circuit(ASIC), field programmable gate array (FPGA), analog circuit, digitalcircuit, combinations thereof, or other now known or later developedprocessor. The controller 400 and/or processor 300 may be a singledevice or combinations of devices, such as associated with a network,distributed processing, or cloud computing. The controller 400 may beconfigured to receive position information from the positioningcircuitry 407 and identify when and where to download or buffer data.

The positioning circuitry 407, that is an example of a positioningsystem, is configured to determine a geographic position of the device122. The positioning circuitry may use, for example, a GPS receiver todetermine the location of the device 122. The positioning circuitry mayinclude movement circuitry. The movement circuitry, that is an example amovement tracking system, is configured to determine movement of adevice 122. The position circuitry 407 and the movement circuitry may beseparate systems, or segments of the same positioning or movementcircuitry system. In an embodiment, components as described herein withrespect to the device 122 may be implemented as a static device.

The input device 403 may be one or more buttons, keypad, keyboard,mouse, stylist pen, trackball, rocker switch, touch pad, voicerecognition circuit, or other device or component for inputting data tothe device 122. The input device 403 and the output interface 411 may becombined as a touch screen, that may be capacitive or resistive. Theoutput interface 411 may be a liquid crystal display (LCD) panel, lightemitting diode (LED) screen, thin film transistor screen, or anothertype of display. The output interface 411 may also include audiocapabilities, or speakers.

The communication interface 405 and/or communication interface 805 mayinclude any operable connection. An operable connection may be one inwhich signals, physical communications, and/or logical communicationsmay be sent and/or received. An operable connection may include aphysical interface, an electrical interface, and/or a data interface.The communication interface 405 and/or communication interface 805provides for wireless and/or wired communications in any now known orlater developed format. The communication interface 405 and/orcommunication interface 805 may include a receiver/transmitter fordigital radio signals or other broadcast mediums. A receiver/transmittermay be externally located from the device 122 such as in or on avehicle. The communications interface 405 may be configured to receiveinstructions from a MNO or cellular provide relating to buffering ordownloading information. The communications interface 405 may receiveinformation relating to network congestion at one or more cells.

The term “computer-readable medium” includes a single medium or multiplemedia, such as a centralized or distributed database, and/or associatedcaches and servers that store one or more sets of instructions. The term“computer-readable medium” shall also include any medium that is capableof storing, encoding or carrying a set of instructions for execution bya processor or that cause a computer system to perform any one or moreof the methods or operations disclosed herein.

In a particular non-limiting, exemplary embodiment, thecomputer-readable medium can include a solid-state memory such as amemory card or other package that houses one or more non-volatileread-only memories. Further, the computer-readable medium can be arandom access memory or other volatile re-writable memory. Additionally,the computer-readable medium can include a magneto-optical or opticalmedium, such as a disk or tapes or other storage device to capturecarrier wave signals such as a signal communicated over a transmissionmedium. A digital file attachment to an e-mail or other self-containedinformation archive or set of archives may be considered a distributionmedium that is a tangible storage medium. Accordingly, the disclosure isconsidered to include any one or more of a computer-readable medium or adistribution medium and other equivalents and successor media, in whichdata or instructions may be stored.

In an alternative embodiment, dedicated hardware implementations, suchas application specific integrated circuits, programmable logic arraysand other hardware devices, can be constructed to implement one or moreof the methods described herein. Applications that may include theapparatus and systems of various embodiments can broadly include avariety of electronic and computer systems. One or more embodimentsdescribed herein may implement functions using two or more specificinterconnected hardware modules or devices with related control and datasignals that can be communicated between and through the modules, or asportions of an application-specific integrated circuit. Accordingly, thepresent system encompasses software, firmware, and hardwareimplementations.

In accordance with various embodiments of the present disclosure, themethods described herein may be implemented by software programsexecutable by a computer system. Further, in an exemplary, non-limitedembodiment, implementations can include distributed processing,component/object distributed processing, and parallel processing.Alternatively, virtual computer system processing can be constructed toimplement one or more of the methods or functionality as describedherein.

Although the present specification describes components and functionsthat may be implemented in particular embodiments with reference toparticular standards and protocols, the invention is not limited to suchstandards and protocols. For example, standards for Internet and otherpacket switched network transmission (e.g., TCP/IP, UDP/IP, HTML, HTTP,HTTPS) represent examples of the state of the art. Such standards areperiodically superseded by faster or more efficient equivalents havingessentially the same functions. Accordingly, replacement standards andprotocols having the same or similar functions as those disclosed hereinare considered equivalents thereof.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, and it can bedeployed in any form, including as a standalone program or as a module,component, subroutine, or other unit suitable for use in a computingenvironment. A computer program does not necessarily correspond to afile in a file system. A program can be stored in a portion of a filethat holds other programs or data (e.g., one or more scripts stored in amarkup language document), in a single file dedicated to the program inquestion, or in multiple coordinated files (e.g., files that store oneor more modules, sub programs, or portions of code). A computer programcan be deployed to be executed on one computer or on multiple computersthat are located at one site or distributed across multiple sites andinterconnected by a communication network.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application specific integrated circuit).

As used in this application, the term ‘circuitry’ or ‘circuit’ refers toall of the following: (a) hardware-only circuit implementations (such asimplementations in only analog and/or digital circuitry) and (b) tocombinations of circuits and software (and/or firmware), such as (asapplicable): (i) to a combination of processor(s) or (ii) to portions ofprocessor(s)/software (including digital signal processor(s)), software,and memory(ies) that work together to cause an apparatus, such as amobile phone or server, to perform various functions) and (c) tocircuits, such as a microprocessor(s) or a portion of amicroprocessor(s), that require software or firmware for operation, evenif the software or firmware is not physically present.

This definition of ‘circuitry’ applies to all uses of this term in thisapplication, including in any claims. As a further example, as used inthis application, the term “circuitry” would also cover animplementation of merely a processor (or multiple processors) or portionof a processor and its (or their) accompanying software and/or firmware.The term “circuitry” would also cover, for example and if applicable tothe particular claim element, a baseband integrated circuit orapplications processor integrated circuit for a mobile phone or asimilar integrated circuit in server, a cellular network device, orother network device.

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andanyone or more processors of any kind of digital computer. Generally, aprocessor receives instructions and data from a read only memory or arandom access memory or both. The essential elements of a computer are aprocessor for performing instructions and one or more memory devices forstoring instructions and data. Generally, a computer also includes, orbe operatively coupled to receive data from or transfer data to, orboth, one or more mass storage devices for storing data, e.g., magnetic,magneto optical disks, or optical disks. However, a computer need nothave such devices. Moreover, a computer can be embedded in anotherdevice, e.g., a mobile telephone, a personal digital assistant (PDA), amobile audio player, a Global Positioning System (GPS) receiver, to namejust a few. Computer readable media suitable for storing computerprogram instructions and data include all forms of non-volatile memory,media and memory devices, including by way of example semiconductormemory devices, e.g., EPROM, EEPROM, and flash memory devices; magneticdisks, e.g., internal hard disks or removable disks; magneto opticaldisks; and CD ROM and DVD-ROM disks. The memory may be a non-transitorymedium such as a ROM, RAM, flash memory, etc. The processor and thememory can be supplemented by, or incorporated in, special purpose logiccircuitry.

To provide for interaction with a user, embodiments of the subjectmatter described in this specification can be implemented on a devicehaving a display, e.g., a CRT (cathode ray tube) or LCD (liquid crystaldisplay) monitor, for displaying information to the user and a keyboardand a pointing device, e.g., a mouse or a trackball, by which the usercan provide input to the computer. Other kinds of devices can be used toprovide for interaction with a user as well; for example, feedbackprovided to the user can be any form of sensory feedback, e.g., visualfeedback, auditory feedback, or tactile feedback; and input from theuser can be received in any form, including acoustic, speech, or tactileinput.

Embodiments of the subject matter described in this specification can beimplemented in a computing system that includes a back end component,e.g., as a data server, or that includes a middleware component, e.g.,an application server, or that includes a front end component, e.g., aclient computer having a graphical user interface or a Web browserthrough which a user can interact with an implementation of the subjectmatter described in this specification, or any combination of one ormore such back end, middleware, or front end components. The componentsof the system can be interconnected by any form or medium of digitaldata communication, e.g., a communication network. Examples ofcommunication networks include a local area network (“LAN”) and a widearea network (“WAN”), e.g., the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

The illustrations of the embodiments described herein are intended toprovide a general understanding of the structure of the variousembodiments. The illustrations are not intended to serve as a completedescription of all of the elements and features of apparatus and systemsthat utilize the structures or methods described herein. Many otherembodiments may be apparent to those of skill in the art upon reviewingthe disclosure. Other embodiments may be utilized and derived from thedisclosure, such that structural and logical substitutions and changesmay be made without departing from the scope of the disclosure.Additionally, the illustrations are merely representational and may notbe drawn to scale. Certain proportions within the illustrations may beexaggerated, while other proportions may be minimized. Accordingly, thedisclosure and the figures are to be regarded as illustrative ratherthan restrictive.

While this specification contains many specifics, these should not beconstrued as limitations on the scope of the invention or of what may beclaimed, but rather as descriptions of features specific to particularembodiments of the invention. Certain features that are described inthis specification in the context of separate embodiments can also beimplemented in combination in a single embodiment. Conversely, variousfeatures that are described in the context of a single embodiment canalso be implemented in multiple embodiments separately or in anysuitable sub-combination. Moreover, although features may be describedabove as acting in certain combinations and even initially claimed assuch, one or more features from a claimed combination can in some casesbe excised from the combination, and the claimed combination may bedirected to a sub-combination or variation of a sub-combination.

Similarly, while operations are depicted in the drawings and describedherein in a particular order, this should not be understood as requiringthat such operations be performed in the particular order shown or insequential order, or that all illustrated operations be performed, toachieve desirable results. In certain circumstances, multitasking andparallel processing may be advantageous. Moreover, the separation ofvarious system components in the embodiments described above should notbe understood as requiring such separation in all embodiments, and itshould be understood that the described program components and systemscan generally be integrated together in a single software product orpackaged into multiple software products.

One or more embodiments of the disclosure may be referred to herein,individually and/or collectively, by the term “invention” merely forconvenience and without intending to voluntarily limit the scope of thisapplication to any particular invention or inventive concept. Moreover,although specific embodiments have been illustrated and describedherein, it should be appreciated that any subsequent arrangementdesigned to achieve the same or similar purpose may be substituted forthe specific embodiments shown. This disclosure is intended to cover anyand all subsequent adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, are apparent to those of skill in the artupon reviewing the description.

The Abstract of the Disclosure is provided to comply with 37 C.F.R. §1.72(b) and is submitted with the understanding that it will not be usedto interpret or limit the scope or meaning of the claims. In addition,in the foregoing Detailed Description, various features may be groupedtogether or described in a single embodiment for the purpose ofstreamlining the disclosure. This disclosure is not to be interpreted asreflecting an intention that the claimed embodiments require morefeatures than are expressly recited in each claim. Rather, as thefollowing claims reflect, inventive subject matter may be directed toless than all of the features of any of the disclosed embodiments. Thus,the following claims are incorporated into the Detailed Description,with each claim standing on its own as defining separately claimedsubject matter.

It is intended that the foregoing detailed description be regarded asillustrative rather than limiting and that it is understood that thefollowing claims including all equivalents are intended to define thescope of the invention. The claims should not be read as limited to thedescribed order or elements unless stated to that effect. Therefore, allembodiments that come within the scope and spirit of the followingclaims and equivalents thereto are claimed as the invention.

We claim:
 1. A method comprising: receiving a description of a trafficincident; predicting an estimated traffic flow pattern for a first timeperiod as a result of the traffic incident based on the description,current traffic flow, and historical traffic flow; determining an impactarea of the traffic incident based on the estimated traffic flowpattern; estimating a future level of network data congestion for theimpact area as a result of the estimated traffic flow pattern;identifying one or more cells of a cellular network associated with thefuture level of network data congestion; and transmitting a message toone or more devices predicted to traverse the one or more cells of thecellular network.
 2. The method of claim 1, further comprising:generating a load balancing scheme that shifts downloads or transmissionof data to different cells other than the one or more cells of thecellular network associated with the future level of network datacongestion.
 3. The method of claim 1, wherein the message comprises arequest to the one or more devices to download map data from a cell ofthe cellular network that is not affected by an increase in the futurelevel of network data congestion.
 4. The method of claim 1, furthercomprising: preemptively transmitting data to the one or more devicesprior to the one or more devices entering the one or more cells of thecellular network.
 5. The method of claim 1, further comprising:preemptively buffering transportation related data to the one or moredevices prior to the one or more devices entering the one or more cellsof the cellular network.
 6. The method of claim 1, wherein predictingthe estimated traffic flow pattern further uses one or more currentweather conditions.
 7. The method of claim 1, wherein estimating thefuture level of network data congestion comprises: identifying ahistorical number of connected devices in the impact area; andcalculating a predicted number of connected devices in the impact areabased on the estimated traffic flow pattern.
 8. The method of claim 7,wherein estimating the future level of network data congestion furthercomprises: identifying, from one or more device profiles of thepredicted number of connected devices, an estimated bandwidth of thepredicted number of connected devices.
 9. The method of claim 1, whereinidentifying the one or more cells comprises: generating a geographicpolygon based on the impact area; overlaying the geographic polygon on acellular map; and identifying the one or more cells from an overlap ofthe geographic polygon and the cellular map.
 10. An apparatuscomprising: at least one processor; and at least one memory includingcomputer program code for one or more programs; the at least one memoryconfigured to store the computer program code configured to, with the atleast one processor, cause the at least one processor to: receive adescription of a traffic incident; predict an estimated traffic flowpattern for a time period for an impact area as a result of the trafficincident based on the description, current traffic flow, and historicaltraffic flow; estimate a future level of network data congestion as aresult of the estimated traffic flow pattern; identify one or more cellsof a cellular network associated with the future level of network datacongestion; and transmit a message to one or more devices predicted totraverse the one or more cells of the cellular network.
 11. Theapparatus of claim 10, wherein the message comprises a request to theone or more devices to download map data from a cell of the cellularnetwork that is not affected by an increase in the future level ofnetwork data congestion.
 12. The apparatus of claim 10, whereinpredicting the estimated traffic flow pattern is further based on one ormore current weather conditions.
 13. The apparatus of claim 10, whereinthe computer program code is configured to estimate the future level ofnetwork data congestion by causing the processor to: identify ahistorical number of connected devices in the impact area; and calculatea predicted number of connected devices in the impact area based on theestimated traffic flow pattern.
 14. The apparatus of claim 13, whereinthe computer program code further comprises: identifying, from one ormore device profiles of the predicted number of connected devices, anestimated bandwidth of the predicted number of connected devices.
 15. Asystem for optimizing a wireless network, the system comprising: areceiver configured to receive a description of a traffic incident; aprocessor configured to predict an estimated traffic flow pattern for atime period as a result of the traffic incident based on thedescription, current traffic flow data, and historical traffic flowdata, determine an impact area based on the estimated traffic flowpattern, estimate a future level of network data congestion for theimpact area as a result of the estimated traffic flow pattern, andidentify one or more cells of a cellular network associated with thefuture level of network data congestion; and a transmitter configured totransmit a message to one or more devices predicted to traverse the oneor more cells.
 16. The system of claim 15, wherein the message comprisesa request to the one or more devices to download map data from a cell ofthe cellular network that is not affected by an increase in the futurelevel of network data congestion.
 17. The system of claim 15, whereinthe processor is configured to predict the estimated traffic flowpattern based on one or more current weather conditions.
 18. The systemof claim 15, wherein the processor is configured to estimate the futurelevel of network data congestion by identifying a historical number ofconnected devices in the impact area and calculating a predicted numberof connected devices in the impact area based on the estimated trafficflow pattern.
 19. The system of claim 18, wherein the processor isfurther configured to identify from one or more device profiles of thepredicted number of connected devices, an estimated bandwidth of thepredicted number of connected devices.
 20. The system of claim 15,wherein the message comprises a request to the one or more devices topreemptively download data prior to entering the impact area.